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Product recommendation method and device, electronic equipment and medium

A recommendation method and product technology, applied in the computer field, can solve the problems of too many and chaotic recommended products, low efficiency and accuracy, and affect the accuracy and efficiency of product recommendation, so as to avoid extensiveness and low efficiency and improve efficiency and accuracy effects

Pending Publication Date: 2022-04-08
AGRICULTURAL BANK OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the process of using the above method to recommend products, firstly, the manual method of recommending products based on personal experience or network will lead to low efficiency and accuracy of product recommendation; When users make product recommendations, products may be recommended randomly, or there may be too many and chaotic product categories, which further affects the accuracy and efficiency of product recommendations.

Method used

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  • Product recommendation method and device, electronic equipment and medium
  • Product recommendation method and device, electronic equipment and medium
  • Product recommendation method and device, electronic equipment and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0032] figure 1 It is a flow chart of a product recommendation method provided according to Embodiment 1 of the present invention. This embodiment is applicable to recommending corresponding products to users. The method can be executed by a product recommendation device, and the product recommendation device can use hardware and / or software, the product recommendation device can be configured in electronic equipment. Such as figure 1 As shown, the method includes:

[0033] S110. Based on the tags corresponding to each user in the user portrait tag library and the preset tag filtering conditions, filter out the target user set corresponding to the preset tag filtering conditions from the user portrait tag library, wherein the The user portrait tag library includes at least one user and a tag set corresponding to the user, the tag set includes at least one tag, and the target user set includes at least one target user.

[0034] In this embodiment, user portraits can be consi...

Embodiment 2

[0053] image 3 It is a flow chart of a product recommendation method according to Embodiment 2 of the present invention, and this Embodiment 2 is refined on the basis of the foregoing embodiments. In this embodiment, the process of constructing and updating a user portrait tag library, and creating a product recommendation task according to a target user set and a target product set is described in detail. It should be noted that for technical details not exhaustively described in this embodiment, reference may be made to any of the foregoing embodiments. Such as image 3 As shown, the method includes:

[0054] S210. Based on the heterogeneous computing framework, calculate the first matching result between the user portrait association information corresponding to each user and the preset label set, and determine the label set corresponding to each user according to the first matching result.

[0055] In this embodiment, user portrait-related information may refer to user...

Embodiment 3

[0076] Figure 4 It is a schematic structural diagram of a product recommendation device provided according to Embodiment 3 of the present invention. Such as Figure 4 As shown, the device includes: a screening module 310, a determination module 320, a creation module 330 and a recommendation module 340;

[0077] The screening module 310 is configured to, based on the tags corresponding to each user in the user portrait tag library and the preset tag filtering conditions, filter out the target user set corresponding to the preset tag filtering conditions from the user portrait tag library, Wherein, the user portrait tag library includes at least one user and a tag set corresponding to the user, the tag set includes at least one tag, and the target user set includes at least one target user;

[0078] The determining module 320 is configured to, for each target user, determine the target product set of the target user according to the preset product catalog and the label set c...

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Abstract

The embodiment of the invention discloses a product recommendation method and device, electronic equipment and a medium. The method comprises the steps of screening a target user set corresponding to a preset label screening condition from a user portrait label library based on a label corresponding to each user in the user portrait label library and the preset label screening condition; for each target user, determining a target product set of the target user according to a preset product directory and a tag set corresponding to the target user; creating a product recommendation task according to the target user set and the target product set; and recommending the target product set to the corresponding target user according to the product recommendation task. A target user set is obtained through screening, a corresponding target product set is determined for each target user in the target user set, a corresponding product recommendation task is created according to the target user set and the target product set, and the target product set can be recommended to the corresponding target user according to the product recommendation task. And the product recommendation efficiency and accuracy are improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a product recommendation method, device, electronic equipment and media. Background technique [0002] With the rapid development of the financial industry, many financial institutions will recommend corresponding financial products to users in order to develop their business. The currently adopted method of recommending products to users is: business personnel in financial institutions recommend products to corresponding users based on personal experience or contacts. However, in the process of using the above method to recommend products, firstly, the manual method of recommending products based on personal experience or network will lead to low efficiency and accuracy of product recommendation; When users make product recommendations, products may be randomly recommended, or the types of recommended products are too many and too chaotic, which ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06Q30/06G06Q40/00
Inventor 李聪聪杨声钢吴利刘亦昕
Owner AGRICULTURAL BANK OF CHINA
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